Modelling biomass and biodiversity in temperate grasslands with Sentinel-1, Sentinel-2 and Unmanned Aerial Vehicles.

2021 ◽  
Author(s):  
Javier Muro ◽  
Lisa Schwarz ◽  
Florian Männer ◽  
Anja Linstädter ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Machine Learning Algorithms ◽  
Ecosystem Functions ◽  
Biodiversity Indicators ◽  
Biodiversity Exploratories ◽  
Improve Model ◽  
First Results ◽  
Land Use Practices ◽  
Sentinel 2

<p>Land use practices in grasslands are major determinants of their biodiversity and ecosystem functions. Relationships between biodiversity, ecosystem functions and land use practices can vary across climatic and management gradients and across scales. New generations of remote sensing sensors can model grasslands’ biomass and biodiversity parameters with relative RMSE that range between 10% and 40%. However, most of these experiments have been carried out in rather small and homogenous areas. In the project SeBAS (Sensing Biodiversity Across Scales) we are using machine learning algorithms (random forest and neural networks) to model biomass and biodiversity indicators along spatial and management gradients and across scales. Field data (above ground biomass and species inventories) was obtained during summer 2020 from the Biodiversity Exploratories: a set of 150 grassland plots across spatial and management gradients in Germany. Remote sensing information at farm level was obtained from microwave Sentinel-1 and multispectral Sentinel-2 satellites, and at plot level from a multispectral camera mounted on a UAV.</p><p>First results show the limitations of satellite images to map vegetation parameters in heterogeneous landscapes, and how the incorporation of UAV information can be used to improve model estimations of biomass production and biodiversity indicators.</p>

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 21 ◽  
Author(s):  
Francisco Rodríguez-Puerta ◽  
Rafael Alonso Ponce ◽  
Fernando Pérez-Rodríguez ◽  
Beatriz Águeda ◽  
Saray Martín-García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Lidar Data ◽  
Sensing Data ◽  
Sentinel 2

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

scholarly journals Sentinel-2 imagery usage on environmental monitoring of land use and occupation in a microwatershed in Central Brazil

Gaia Scientia ◽  
2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Jean Jesus Novais ◽  
Marilusa Pinto Coelho Lacerda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Social Aspects ◽  
Agricultural Activities ◽  
Maximum Likelihood Algorithm ◽  
Kappa Index ◽  
Central Brazil ◽  
Strategic Plans ◽  
Distrito Federal ◽  
Sentinel 2

In the last decades, sustainability concerns have increased the demand for projects and strategic plans that integrate economic and social aspects, reducing environmental impacts. In this sense, this study aims to monitor land-use adequacy in the Ribeirão Extrema microwatershed, Distrito Federal, based on cross-mapping between land-use and occupation in 2019 and agricultural aptitude map through Geographic Information Systems and Remote sensing. To this end, a hypsometric and thematic database was prepared for the region. Besides, we acquired an image from the Sentinel-2 orbital sensor of October 2019. The image was subjected to classification regarding land-use and occupation, using the MAXVER (maximum likelihood) algorithm. It was observed that 80% of use in 2019 was related to agricultural activities. Kappa index validation reached 81% accuracy. Based on the methodology, we identified 62.33% of agricultural activities occur into its capacity; 4.33%, were used above capacity, causing environmental degradation, especially in permanent preservation areas. The application of the technique was considered satisfactory because the adequacy of land-use in the studied microwatershed could be assessed in order to pursue sustainable development. Continuous analyzes can improve results.

DIAGNÓSTICO DO USO E COBERTURA DA TERRA NOS RESERVATÓRIOS PÚBLICOS DO MUNICÍPIO DE PAU DOS FERROS, RN, BRASIL

2020 ◽  
Vol 11 (38) ◽  
pp. 146-161
Author(s):  
Aluizio Bezerra Júnior ◽  
◽  
Agassiel Medeiros Alves ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Natural Resources ◽  
Spatial Resolution ◽  
Sustainable Use ◽  
Rio Grande ◽  
Spatial Dimensions ◽  
Methodological Procedures ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Research objective is to classify, measure and map the spatial dimensions of land use and land cover classes in public reservoirs 25 de Março and Dr. Pedro Diógenes Fernandes, both belonging to the municipality of Pau dos Ferros, state of Rio Grande do Norte. For the methodological procedures, remote sensing techniques (SIG Qgis version Lyon 2.12.3) were used, of the medium spatial resolution images of the SENTINEL 2 satellite, MSI sensor (Multispectral Instrument), accompanied by the interpretation key. The results showed that there is a possibility of sustainable use, since the exploration and conservation remains in balance, therefore, this research can subsidize the conservation of the use of natural resources around the reservoirs.

scholarly journals Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Nhat-Duc Hoang ◽  
Xuan-Linh Tran
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Green Space ◽  
Urban Land ◽  
Urban Green Space ◽  
Urban Land Use ◽  
Urban Green ◽  
Marine Predators ◽  
Sentinel 2

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

scholarly journals Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

2021 ◽  
Vol 13 (7) ◽  
pp. 1229
Author(s):  
Huan Wang ◽  
Xin Zhang ◽  
Wei Wu ◽  
Hongbin Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Satellite Imagery ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Squared Error ◽  
Land Use Types ◽  
Sentinel 2

Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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